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Health

An evaluation of the replicability of analyses using synthetic health data

techbalu06By techbalu06March 24, 2024No Comments15 Mins Read

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  • Foraker, R. E. et al. Spot the difference: Comparing results of analyses from real patient data and synthetic derivatives. JAMIA Open https://doi.org/10.1093/jamiaopen/ooaa060 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tucker, A. et al. Generating high-fidelity synthetic patient data for assessing machine learning healthcare software. npj Digit. Med. 3, 1–13. https://doi.org/10.1038/s41746-020-00353-9 (2020).

    Article 

    Google Scholar 

  • Wang, Z., Myles, P. & Tucker, A. Generating and evaluating synthetic UK primary care data: Preserving data utility patient privacy. In 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba. 126–31. https://doi.org/10.1109/CBMS.2019.00036 (2019).

  • Wang, Z., Myles, P. & Tucker, A. Generating and evaluating cross-sectional synthetic electronic healthcare data: Preserving data utility and patient privacy. Comput. Intell. 37, 819–851 (2021).

    MathSciNet 

    Google Scholar 

  • Reiner Benaim, A. et al. Analyzing medical research results based on synthetic data and their relation to real data results: Systematic comparison from five observational studies. JMIR Med. Inform. 8, e16492 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Mendelevitch, O. & Lesh, M.D. Fidelity and Privacy of Synthetic Medical Data. arXiv:210108658 [cs] (2021).

  • Muniz-Terrera, G. et al. Virtual cohorts and synthetic data in dementia: An illustration of their potential to advance research. Front. Artif. Intell. 4, 613956 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Foraker, R. et al. Analyses of original and computationally-derived electronic health record data: The National COVID Cohort Collaborative. J. Med. Internet Res. https://doi.org/10.2196/30697 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Azizi, Z. et al. Can synthetic data be a proxy for real clinical trial data ? A validation study. BMJ Open 11, e043497 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • El Emam, K. et al. Evaluating the utility of synthetic COVID-19 case data. JAMIA Open. 4, ooab012 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Beaulieu-Jones, B. K. et al. Privacy-preserving generative deep neural networks support clinical data sharing. Circ. Cardiovasc. Qual. Outcomes 12, e005122 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Polonetsky, J. & Renieris, E. 10 Privacy Risks and 10 Privacy Technologies to Watch in the Next Decade. Future of Privacy Forum (2020).

  • Guo, A. et al. The use of synthetic electronic health record data and deep learning to improve timing of high-risk heart failure surgical intervention by predicting proximity to catastrophic decompensation. Front. Digit. Health https://doi.org/10.3389/fdgth.2020.576945 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Haendel, M. A. et al. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J. Am. Med. Inform. Assoc. 28, 427–443 (2021).

    PubMed 

    Google Scholar 

  • CMS. CMS 2008–2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF). https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/DE_Syn_PUF. Accessed 17 July 2022 (2022).

  • Generating and Evaluating Synthetic UK Primary Care Data: Preserving Data Utility & Patient Privacy-IEEE Conference Publication. https://ieeexplore-ieee-org.proxy.bib.uottawa.ca/abstract/document/8787436. Accessed 31 Aug 2019 (2019).

  • Synthetic data at CPRD. Medicines & Healthcare products Regulatory Agency. https://www.cprd.com/content/synthetic-data. Accessed 24 Sep 2020 (2020).

  • NHS England. A&E Synthetic Data. https://data.england.nhs.uk/dataset/a-e-synthetic-data. Accessed 16 July 2022 (2022)

  • Synthetic dataset. Integraal Kankercentrum Nederland. https://iknl.nl/en/ncr/synthetic-dataset . Accessed 20 Nov 2021 (2021).

  • The Simulacrum. The Simulacrum. https://simulacrum.healthdatainsight.org.uk/ . Accessed 27 Nov 2021 (2021).

  • SNDS synthétiques. Systeme National des Donnees de Sante. https://documentation-snds.health-data-hub.fr/formation_snds/donnees_synthetiques/. Accessed 20 Jan 2022 (2021).

  • #opendata4covid19 Website User Manual. https://rtrod-assets.s3.ap-northeast-2.amazonaws.com/static/tools/manual/COVID-19+website+manual_v2.1.pdf . Accessed 8 Apr 2020 (2020).

  • Lun, R. et al. Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. PLOS ONE. 19, e0295921 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Karr, A. et al. A framework for evaluating the utility of data altered to protect confidentiality: The American Statistician: Vol. 60, No. 3. Am. Stat. 60, 224–232 (2006).

    Google Scholar 

  • Emam, K. E. et al. Utility metrics for evaluating synthetic health data generation methods: Validation study. JMIR Med. Inform. 10, e35734 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Goncalves, A. et al. Generation and evaluation of synthetic patient data. BMC Med. Res. Methodol. https://doi.org/10.1186/s12874-020-00977-1 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Platzer, M. & Reutterer, T. Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data. arXiv:210400635 [cs, stat] (2021).

  • El Emam, K., Mosquera, L. & Zheng, C. Optimizing the synthesis of clinical trial data using sequential trees. J. Am. Med. Inform. Assoc. https://doi.org/10.1093/jamia/ocaa249 (2020).

    Article 

    Google Scholar 

  • National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. http://www.ncbi.nlm.nih.gov/books/NBK547537/. Accessed 28 July 2023 (National Academies Press (US), 2019).

  • Grund, S., Lüdtke, O. & Robitzsch, A. Using synthetic data to improve the reproducibility of statistical results in psychological research. Psychol. Methods (2022).

  • Morris, T. P., White, I. R. & Crowther, M. J. Using simulation studies to evaluate statistical methods. Stat. Med. 38, 2074–2102 (2019).

    MathSciNet 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rubin, D. Discussion: Statistical disclosure limitation. J. Off. Stat. 9, 462–468 (1993).

    Google Scholar 

  • Raghunathan, T., Reiter, J. & Rubin, D. Multiple imputation for statistical disclosure control. J. Off. Stat. 19, 1–16 (2003).

    Google Scholar 

  • Reiter, J. P. Satisfying disclosure restrictions with synthetic data sets. J. Off. Stat. 18, 531–543 (2002).

    Google Scholar 

  • Raab, G. M., Nowok, B. & Dibben, C. Practical data synthesis for large samples. J. Priv. Confident. 7, 67–97 (2016).

    Google Scholar 

  • Reiter, J. P. New approaches to data dissemination: A glimpse into the future (?). Chance 17, 11–15 (2004).

    MathSciNet 

    Google Scholar 

  • Park, N. et al. Data synthesis based on generative adversarial networks. Proc. VLDB Endow. 11, 1071–1083 (2018).

    Google Scholar 

  • Hu, J. Bayesian Estimation of Attribute and Identification Disclosure Risks in Synthetic Data. arXiv:180402784 [stat] (2018).

  • Taub, J. et al. Differential correct attribution probability for synthetic data: An exploration. In Privacy in Statistical Databases (eds Domingo-Ferrer, J. & Montes, F.) 122–137 (Springer, 2018).

    Google Scholar 

  • Hu, J., Reiter, J. P. & Wang, Q. Disclosure risk evaluation for fully synthetic categorical data. In Privacy in Statistical Databases (ed. Domingo-Ferrer, J.) 185–199 (Springer, 2014).

    Google Scholar 

  • Wei, L. & Reiter, J. P. Releasing synthetic magnitude microdata constrained to fixed marginal totals. Stat. J. IAOS 32, 93–108 (2016).

    CAS 

    Google Scholar 

  • Ruiz, N., Muralidhar, K. & Domingo-Ferrer, J. On the privacy guarantees of synthetic data: A reassessment from the maximum-knowledge attacker perspective. In Privacy in Statistical Databases (eds Domingo-Ferrer, J. & Montes, F.) 59–74 (Springer, 2018).

    Google Scholar 

  • Reiter, J. P. Releasing multiply imputed, synthetic public use microdata: An illustration and empirical study. J. R. Stat. Soc. Ser. A (Statistics in Society) 168, 185–205 (2005).

    MathSciNet 

    Google Scholar 

  • Zhang, Z. et al. Ensuring electronic medical record simulation through better training, modeling, and evaluation. J. Am. Med. Inform. Assoc. https://doi.org/10.1093/jamia/ocz161 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, Z. et al. SynTEG: A framework for temporal structured electronic health data simulation. J. Am. Med. Inform. Assoc. https://doi.org/10.1093/jamia/ocaa262 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Goncalves, A. et al. Generation and evaluation of synthetic patient data. BMC Med. Res. Methodol. 20, 108 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Hilprecht, B., Härterich, M. & Bernau, D. Monte Carlo and reconstruction membership inference attacks against generative models. Proc. Priv. Enhanc. Technol. 2019, 232–249 (2019).

    Google Scholar 

  • Taub, J., Elliot, M. & Sakshaug, W. The impact of synthetic data generation on data utility with application to the 1991 UK samples of anonymised records. Trans Data Priv. 13, 1–23 (2020).

    Google Scholar 

  • Drechsler, J. et al. A new approach for disclosure control in the IAB establishment panel—Multiple imputation for a better data access. AStA Adv. Stat. Anal. 92, 439–458 (2008).

    MathSciNet 

    Google Scholar 

  • Loong, B. & Rubin, D. B. Multiply-imputed synthetic data: Advice to the imputer. J. Off. Stat. 33, 1005–1019 (2017).

    Google Scholar 

  • Loong, B. et al. Disclosure control using partially synthetic data for large-scale health surveys, with applications to CanCORS. Stat. Med. 32, 4139–4161 (2013).

    MathSciNet 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Reiter, J. Inference for partially synthetic, public use microdata sets. Surv. Methodol. 29, 181–188 (2003).

    Google Scholar 

  • van der Ploeg, T., Austin, P. C. & Steyerberg, E. W. Modern modelling techniques are data hungry: A simulation study for predicting dichotomous endpoints. BMC Med. Res. Methodol. 14, 137 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • CEO Life Sciences Consortium. Share, Integrate & Analyze Cancer Research Data. Project Data Sphere. https://projectdatasphere.org/projectdatasphere/html/home. Accessed 11 July 2019 (2019).

  • Alberts, S. R. et al. Effect of oxaliplatin, fluorouracil, and leucovorin with or without cetuximab on survival among patients with resected stage III colon cancer: A randomized trial. JAMA 307, 1383–1393 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • El-Hussuna, A. et al. Extended right-sided colon resection does not reduce the risk of colon cancer local-regional recurrence: Nationwide population-based study from Danish Colorectal Cancer Group Database. Dis. Colon Rectum 6, 10–1097 (2022).

    Google Scholar 

  • Chen, H., Cohen, P. & Chen, S. How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Commun. Stat.-Simul. Comput. 39, 860–864 (2010).

    MathSciNet 

    Google Scholar 

  • Schäfer, T. & Schwarz, M. A. The meaningfulness of effect sizes in psychological research: Differences between sub-disciplines and the impact of potential biases. Front. Psychol. 10, 113 (2019).

    Google Scholar 

  • Song, F. et al. Dissemination and publication of research findings : An updated review of related biases. Health Technol. Assess. 14, 1–220 (2010).

    Google Scholar 

  • Demidenko, E. Sample size determination for logistic regression revisited. Stat. Med. 26, 3385–3397 (2007).

    MathSciNet 
    PubMed 

    Google Scholar 

  • Hsieh, F. Y., Bloch, D. A. & Larsen, M. D. A simple method of sample size calculation for linear and logistic regression. Stat. Med. 17, 1623–1634 (1998).

    CAS 
    PubMed 

    Google Scholar 

  • Collins, G. S. et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ 350, g7594 (2015).

    PubMed 

    Google Scholar 

  • Christodoulou, E. et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110, 12–22 (2019).

    PubMed 

    Google Scholar 

  • Dankar, F. K. & Ibrahim, M. Fake it till you make it: Guidelines for effective synthetic data generation. Appl. Sci. 11, 2158. https://doi.org/10.3390/app11052158 (2021).

    Article 
    CAS 

    Google Scholar 

  • Dahdaleh, F. S. et al. Obstruction predicts worse long-term outcomes in stage III colon cancer: A secondary analysis of the N0147 trial. Surgery 164, 1223–1229 (2018).

    PubMed 

    Google Scholar 

  • Maclagan, L. C. et al. The CANHEART health index: A tool for monitoring the cardiovascular health of the Canadian population. CMAJ 186, 180–187 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Azizi, Z. et al. A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health. Sci. Rep. 13, 11540. https://doi.org/10.1038/s41598-023-38457-3 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • European Society of Coloproctology Collaborating Group. Predictors for anastomotic leak, postoperative complications, and mortality after right colectomy for cancer: Results from an International Snapshot Audit. Dis. Colon Rectum 63, 606–618 (2020).

    Google Scholar 

  • 2017 and 2015 European Society of Coloproctology (ESCP) collaborating groups. The impact of conversion on the risk of major complication following laparoscopic colonic surgery: An international, multicentre prospective audit. Colorectal Dis. 20 (Suppl 6), 69–89 (2018).

  • Reiter, J. Using CART to generate partially synthetic, public use microdata. J. Off. Stat. 21, 441–462 (2005).

    Google Scholar 

  • Drechsler, J. & Reiter, J. P. An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Comput. Stat. Data Anal. 55, 3232–3243 (2011).

    MathSciNet 

    Google Scholar 

  • Arslan, R. C. et al. Using 26,000 diary entries to show ovulatory changes in sexual desire and behavior. J. Pers. Soc. Psychol. 121, 410–431 (2021).

    PubMed 

    Google Scholar 

  • Bonnéry, D. et al. The promise and limitations of synthetic data as a strategy to expand access to state-level multi-agency longitudinal data. J. Res. Educ. Effect. 12, 616–647 (2019).

    Google Scholar 

  • Sabay, A. et al. Overcoming small data limitations in heart disease prediction by using surrogate data. SMU Data Sci. Rev. 1, 12 (2018).

    Google Scholar 

  • Freiman, M., Lauger, A. & Reiter, J. Data Synthesis and Perturbation for the American Community Survey at the U.S. Census Bureau. US Census Bureau. https://www.census.gov/library/working-papers/2018/adrm/formal-privacy-synthetic-data-acs.html. Accessed 24 Feb 2020 (2017).

  • Nowok, B. Utility of Synthetic Microdata Generated Using Tree-Based Methods. https://unece.org/statistics/events/SDC2015 (Helsinki, 2015).

  • Nowok, B., Raab, G. M. & Dibben, C. Providing bespoke synthetic data for the UK longitudinal studies and other sensitive data with the synthpop package for R 1. Stat. J. IAOS 33, 785–796 (2017).

    Google Scholar 

  • Quintana, D. S. A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis generation. eLife 9, e53275 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Little, C., Elliot, M., Allmendinger, R. et al. Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study. Vol. 17. https://unece.org/statistics/documents/2021/12/working-documents/generative-adversarial-networks-synthetic-data. (United Nations Economic Commission for Europe, 2021).

  • Hernandez, M. et al. Synthetic data generation for tabular health records: A systematic review. Neurocomputing. 493, 28–45 (2022).

    Google Scholar 

  • Jacobs, F. et al. Opportunities and challenges of synthetic data generation in oncology. JCO Clin. Cancer Inform. 3, e2300045 (2023).

    Google Scholar 

  • Ghosheh, G. O., Li, J. & Zhu, T. A survey of generative adversarial networks for synthesizing structured electronic health records. ACM Comput. Surv. 56, 1471–14734 (2024).

    Google Scholar 

  • Chin-Cheong, K., Sutter, T. & Vogt, J.E. Generation of Heterogeneous Synthetic Electronic Health Records using GANs. https://doi.org/10.3929/ethz-b-000392473 (2019).

  • Choi, E., Biswal, S., Malin, B. et al. Generating Multi-Label Discrete Patient Records Using Generative Adversarial Networks. arXiv:170306490 [cs] (2017).

  • Yan, C., Zhang, Z., Nyemba, S. et al. Generating Electronic Health Records with Multiple Data Types and Constraints. arXiv:200307904 [cs, stat] (2020).

  • Bühlmann, P. & Hothorn, T. Boosting algorithms: Regularization. Predict. Model Fit. Stat. Sci. 22, 477–505 (2007).

    Google Scholar 

  • Ke, G., Meng, Q., Finley, T. et al. LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems (Guyon, I., Luxburg, U.V., Bengio, S. et al. eds.). Vol. 30. 3146–3154. http://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf. Accessed 15 Oct 2020 (Curran Associates, Inc., 2017).

  • Snoek, J., Larochelle, H. & Adams, R.P. Practical Bayesian optimization of machine learning algorithms. In Proceedings of the 25th International Conference on Neural Information Processing Systems. Vol. 2. 2951–2959. https://papers.nips.cc/paper_files/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html (Curran Associates Inc., 2012).

  • Jones, M. C. Simple boundary correction for kernel density estimation. Stat. Comput. 3, 135–146 (1993).

    Google Scholar 

  • Xu, L., Skoularidou, M., Cuesta-Infante, A. et al. Modeling tabular data using conditional GAN. In Advances in Neural Information Processing Systems (Wallach, H., Larochelle, H., d’Alche-Buc, F. et al. eds.). 7335–7345. https://papers.nips.cc/paper/2019/hash/254ed7d2de3b23ab10936522dd547b78-Abstract.html. Accessed 2 Oct 2021 (Curran Associates, Inc., 2019).

  • Bourou, S. et al. A review of tabular data synthesis using GANs on an IDS dataset. Information 12, 375 (2021).

    Google Scholar 

  • Mirza, M. & Osindero, S. Conditional Generative Adversarial Nets. https://doi.org/10.48550/arXiv.1411.1784 (2014).

  • Xu, L., Skoularidou, M., Cuesta-Infante, A. et al. Modeling tabular data using conditional GAN. In Advances in Neural Information Processing Systems. https://papers.nips.cc/paper/2019/hash/254ed7d2de3b23ab10936522dd547b78-Abstract.html (2019).

  • El Kababji, S., Mitsakakis, N., Fang, X. et al. Evaluating the utility and privacy of synthetic breast cancer clinical trial datasets. JCO CCI (accepted).

  • El Emam, K., Mosquera, L. & Fang, X. Validating a membership disclosure metric for synthetic health data. JAMIA Open. 5, ooac083 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Cancer of the Colon and Rectum-Cancer Stat Facts. SEER. https://seer.cancer.gov/statfacts/html/colorect.html. Accessed 9 Oct 2021 (2021).

  • Iversen, L. H. et al. Improved survival of colorectal cancer in Denmark during 2001–2012—The efforts of several national initiatives. Acta Oncol. 55(Suppl 2), 10–23 (2016).

    PubMed 

    Google Scholar 

  • Burton, A. et al. The design of simulation studies in medical statistics. Stat. Med. 25, 4279–4292 (2006).

    MathSciNet 
    PubMed 

    Google Scholar 

  • Boulesteix, A.-L., Lauer, S. & Eugster, M. J. A. A plea for neutral comparison studies in computational sciences. PLOS ONE 8, e61562 (2013).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Patki, N., Wedge, R. & Veeramachaneni, K. The synthetic data vault. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 399–410. https://doi.org/10.1109/DSAA.2016.49 (IEEE, 2016).

  • Yan, C., Yan, Y., Wan, Z. et al. A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models. https://doi.org/10.48550/arXiv.2208.01230 (2022).

  • De Cristofaro, E. A critical overview of privacy in machine learning. IEEE Secur. Privacy 19, 19–27 (2021).

    Google Scholar 

  • Shafee, A. & Awaad, T. A. Privacy attacks against deep learning models and their countermeasures. J. Syst. Architect. 114, 101940 (2021).

    Google Scholar 

  • Veale, M., Binns, R. & Edwards, L. Algorithms that remember: Model inversion attacks and data protection law. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20180083 (2018).

    ADS 

    Google Scholar 

  • Klein, R. A. et al. Investigating variation in replicability: A “many labs” replication project. Soc. Psychol. 45, 142–152 (2014).

    Google Scholar 

  • Camerer, C. F. et al. Evaluating the replicability of social science experiments in nature and science between 2010 and 2015. Nat. Hum. Behav. 2, 637–644. https://doi.org/10.1038/s41562-018-0399-z (2018).

    Article 
    PubMed 

    Google Scholar 

  • Open Science Collaboration. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).

  • Franklin, J. M. et al. Nonrandomized real-world evidence to support regulatory decision making: Process for a randomized trial replication project. Clin. Pharmacol. Ther. 107, 817–826 (2020).

    PubMed 

    Google Scholar 

  • Crown, W. et al. Can observational analyses of routinely collected data emulate randomized trials? Design and feasibility of the observational patient evidence for regulatory approval science and understanding disease project. Value Health. 26, 176–184 (2023).

    PubMed 

    Google Scholar 

  • Yoon, D. et al. Real-world data emulating randomized controlled trials of non-vitamin K antagonist oral anticoagulants in patients with venous thromboembolism. BMC Med. 21, 375 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang, S. V., Schneeweiss, S., RCT-DUPLICATE Initiative. Emulation of randomized clinical trials with nonrandomized database analyses: Results of 32 clinical trials. JAMA 329, 1376–1385 (2023).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Franklin, J. M. et al. Emulating randomized clinical trials with nonrandomized real-world evidence studies. Circulation. 143, 1002–1013 (2021).

    PubMed 

    Google Scholar 

  • Patil, P., Peng, R. D. & Leek, J. T. What should researchers expect when they replicate studies? A statistical view of replicability in psychological science. Perspect. Psychol. Sci. 11, 539–544 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

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